Towards Robust Knowledge Tracing Models via k-Sparse Attention

TL;DR

This paper introduces sparseKT, a framework that enhances the robustness and generalization of attention-based knowledge tracing models by selectively focusing on the most relevant student interactions through sparsification techniques.

Contribution

The paper proposes a novel sparsification approach for attention in knowledge tracing models, improving robustness and reducing overfitting on small datasets.

Findings

SparseKT improves model robustness and generalization.

It achieves comparable accuracy to state-of-the-art models.

The approach effectively filters irrelevant interactions.

Abstract

Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interaction sequences. With the advanced capability of capturing contextual long-term dependency, attention mechanism becomes one of the essential components in many deep learning based KT (DLKT) models. In spite of the impressive performance achieved by these attentional DLKT models, many of them are often vulnerable to run the risk of overfitting, especially on small-scale educational datasets. Therefore, in this paper, we propose \textsc{sparseKT}, a simple yet effective framework to improve the robustness and generalization of the attention based DLKT approaches. Specifically, we incorporate a k-selection module to only pick items with the highest attention scores. We propose two sparsification heuristics : (1) soft-thresholding sparse attention and (2) top-$K$ sparse attention. We show that our \textsc{sparseKT} is able to help attentional KT models get rid of irrelevant student interactions and have comparable predictive performance when compared to 11 state-of-the-art KT models on three publicly available real-world educational datasets. To encourage reproducible research, we make our data and code publicly available at \url{https://github.com/pykt-team/pykt-toolkit}\footnote{We merged our model to the \textsc{pyKT} benchmark at \url{https://pykt.org/}.}.